Machine learning-based cloud forecast corrections for fusions of numerical weather prediction model and satellite data

Given the diversity of cloud forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning-based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numericalweather prediction model error trends aswell as improving the accuracy and sensitivity of the forecasts. The framework implements a Unet-Convolutional Neural Network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite (GOES-16) as well as clouds predicted by the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3-12 hours). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application, a comparative assessment of results for upper tropospheric clouds.